Joint Device Pairing and Bandwidth Allocation Optimisation for Semantic Feature Multiple Access Networks

TL;DR

This paper introduces SFMA, a novel multi-user semantic communication framework with joint device pairing and bandwidth optimization, improving image reconstruction quality in wireless networks.

Contribution

It extends SwinJSCC to a two-user superimposition model with a new CUA module and proposes an efficient algorithm for joint pairing and resource allocation.

Findings

SFMA significantly enhances image reconstruction quality.

The optimization reduces semantic distortion under physical constraints.

The proposed algorithm is efficient and effective in simulations.

Abstract

This paper presents a Semantic Feature Multiple Access (SFMA) framework for multi-user semantic communication in downlink wireless systems. By extending SwinJSCC to a two-user superimposition paradigm, SFMA enables simultaneous semantic transmission to multiple users over shared time-frequency resources. A key innovation is the Cross-User Attention (CUA) module, which facilitates controlled semantic feature exchange between paired users by leveraging inter-image similarity while mitigating interference. We formulate a joint user pairing and resource allocation problem to minimize global semantic distortion under constraints on bandwidth, end-to-end latency, and energy. This mixed-integer non-convex problem is decomposed into a Minimum-Weight Perfect Matching (MWPM) sub-problem and a convex bandwidth allocation feasibility check, with semi-closed-form bandwidth bounds derived from a strictly concave rate expression. A polynomial-time algorithm based on Blossom matching and bisection search is proposed. Extensive simulations on ImageNet-100 show that SFMA significantly improves reconstruction quality across pairing modes, and the proposed optimization effectively reduces overall distortion while satisfying physical-layer constraints.